U.S. patent application number 16/800282 was filed with the patent office on 2021-05-20 for defect observation machine and image analysis and compensation method thereof.
This patent application is currently assigned to Shanghai Huali Microelectronics Corporation. The applicant listed for this patent is Shanghai Huali Microelectronics Corporation. Invention is credited to Yunwei Ding.
Application Number | 20210152745 16/800282 |
Document ID | / |
Family ID | 1000004732639 |
Filed Date | 2021-05-20 |
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United States Patent
Application |
20210152745 |
Kind Code |
A1 |
Ding; Yunwei |
May 20, 2021 |
Defect Observation Machine and Image Analysis and Compensation
Method Thereof
Abstract
Embodiments described herein relate to a defect observation
machine and an image analysis and compensation method thereof. By
embedding a real-time image analysis unit in a defect observation
machine, analyzing and judging a defect pattern picture outputted
by a defect observation unit in real time, and feeding back a
focusing compensation value of a blurred defect pattern picture to
the defect observation unit to perform focusing compensation on the
blurred defect pattern picture, a clear photo is presented.
Inventors: |
Ding; Yunwei; (Shanghai,
CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Shanghai Huali Microelectronics Corporation |
Shanghai |
|
CN |
|
|
Assignee: |
Shanghai Huali Microelectronics
Corporation
Shanghai
CN
|
Family ID: |
1000004732639 |
Appl. No.: |
16/800282 |
Filed: |
February 25, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04N 5/23218 20180801;
G06K 9/6212 20130101; G06K 9/00134 20130101; H04N 5/232127
20180801; G06K 2209/19 20130101 |
International
Class: |
H04N 5/232 20060101
H04N005/232; G06K 9/00 20060101 G06K009/00; G06K 9/62 20060101
G06K009/62 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 19, 2019 |
CN |
201911133518.1 |
Claims
1. A defect observation machine having a function of analyzing and
compensating for a defect pattern picture in real time so that the
defect pattern picture outputted by the defect observation machine
is a clear defect pattern picture, wherein the defect observation
machine comprises: a defect observation unit, which is used to
perform defect pattern observation on a semiconductor product
through a focusing value and output the defect pattern picture; and
a real-time image analysis unit, which is used to pre-store an
average pixel value and data of a correspondence between a pixel
difference and a focusing compensation value of a clear defect
pattern picture, receive the defect pattern picture, perform data
analysis on the defect pattern picture to obtain an average pixel
value of the defect pattern picture, compare the average pixel
value of the defect pattern picture and the average pixel value of
the corresponding clear defect pattern picture to obtain a pixel
difference, judge the pixel difference, output the received defect
pattern picture as a final defect pattern picture if the absolute
value of the pixel difference is less than or equal to a threshold,
search for a corresponding focusing compensation value from the
data of the correspondence between the pixel difference and the
focusing compensation value according to the pixel difference if
the absolute value of the pixel difference is greater than the
threshold, output the focusing compensation value to the defect
observation unit, such that the defect observation unit adjusts a
focusing value of the defect observation unit by using the focusing
compensation value and then keeps observing the same defect pattern
and again outputs a defect pattern picture to the real-time image
analysis unit.
2. The defect observation machine according to claim 1, wherein the
defect observation unit is an electron microscope.
3. The defect observation machine according to claim 1, wherein the
real-time image analysis unit selects 20% of a central portion of
the received defect pattern picture, and obtains an average pixel
value of the defect pattern picture within the range of 20% as the
average pixel value of the defect pattern picture.
4. The defect observation machine according to claim 3, wherein the
defect pattern picture within the range of 20% is divided into n*n
units to obtain pixel values of the n*n units for converting the
defect pattern picture within the range of 20% into data of
luminance values of n*n pixels, so as to calculate an average value
of pixel values of the data of luminance values of n*n pixels as
the average pixel value of the defect pattern picture.
5. The defect observation machine according to claim 3, wherein the
defect pattern picture within the range of 20% is divided into n*n
units to obtain pixel values of the n*n units for converting the
defect pattern picture within the range of 20% into a histogram
with abscissas indicating luminance and ordinates indicating pixel
values, so as to obtain the average pixel value of the defect
pattern picture according to the histogram.
6. The defect observation machine according to claim 1, wherein the
average pixel value of the clear picture of the defect pattern
comprises average pixel values of clear pictures of the defect
pattern of defects of 90% of all in-line defect types.
7. The defect observation machine according to claim 1, wherein an
average pixel value of a blurred in-line defect pattern picture and
an average pixel value of a clear defect pattern picture resulting
from compensation are counted to obtain a pixel difference, and a
focusing compensation value for compensating the blurred defect
pattern picture as the clear defect pattern picture is counted, so
as to obtain data of a correspondence between the pixel difference
and the focusing compensation value.
8. The defect observation machine according to claim 1, wherein the
pixel difference comprises a plurality of ranges wherein each
corresponding to one focusing compensation value.
9. The defect observation machine according to claim 8, wherein
when the pixel difference is negative, the focusing compensation
value is positive, and when the pixel difference is positive, the
focusing compensation value is negative.
10. The defect observation machine according to claim 1, wherein
the threshold is obtained through definition experiments and
analyses of various in-line defect pattern pictures.
11. The defect observation machine according to claim 1, wherein
the threshold is 10.
12. An image analysis and compensation method of a defect
observation machine, wherein the method comprises: S1: a defect
observation unit performing defect pattern observation on a
semiconductor product through a focusing value, so as to output a
defect pattern picture; S2: a real-time image analysis unit
receiving the defect pattern picture, and performing data analysis
on the defect pattern picture to obtain an average pixel value of
the defect pattern picture; S3: pre-storing in the real-time image
analysis unit an average pixel value of a clear picture of a defect
pattern and data of a correspondence between a pixel difference and
a focusing compensation value; and S4: comparing the average pixel
value of the defect pattern picture and the average pixel value of
the corresponding clear picture of the defect picture to obtain a
pixel difference, the real-time image analysis unit judging the
pixel difference, the real-time image analysis unit outputting the
received defect pattern picture as a final defect pattern picture
if the absolute value of the pixel difference is less than or equal
to a threshold, the real-time image analysis unit searching for a
corresponding focusing compensation value from the data of the
correspondence between the pixel difference and the focusing
compensation value according to the pixel difference if the
absolute value of the pixel difference is greater than the
threshold, and outputting the focusing compensation value to the
defect observation unit, such that the defect observation unit
adjusts a focusing value of the defect observation unit by using
the focusing compensation value and then keeps observing the same
defect pattern and again outputs a defect pattern picture to the
real-time image analysis unit.
13. The image analysis and compensation method of a defect
observation machine according to claim 12, wherein the defect
observation unit is an electron microscope.
14. The image analysis and compensation method of a defect
observation machine according to claim 12, wherein the real-time
image analysis unit selects 20% of a central portion of the
received defect pattern picture, and obtains an average pixel value
of the defect pattern picture within the range of 20% as the
average pixel value of the defect pattern picture.
15. The image analysis and compensation method of a defect
observation machine according to claim 14, wherein the defect
pattern picture within the range of 20% is divided into n*n units
to obtain pixel values of the n*n units for converting the defect
pattern picture within the range of 20% into data of luminance
values of n*n pixels, so as to calculate an average value of pixel
values of the data of luminance values of n*n pixels as the average
pixel value of the defect pattern picture.
16. The image analysis and compensation method of a defect
observation machine according to claim 14, wherein the defect
pattern picture within the range of 20% is divided into n*n units
to obtain pixel values of the n*n units for converting the defect
pattern picture within the range of 20% into a histogram with
abscissas indicating luminance and ordinates indicating pixel
values, so as to obtain the average pixel value of the defect
pattern picture according to the histogram.
17. The image analysis and compensation method of a defect
observation machine according to claim 12, wherein the average
pixel value of the clear picture of the defect pattern comprises
average pixel values of clear pictures of the defect pattern of
defects of 90% of all in-line defect types.
18. The image analysis and compensation method of a defect
observation machine according to claim 17, wherein average pixel
values of the clear pictures of the defect pattern of defects of
90% of all defect types are obtained according to in-line data, and
are pre-stored in the real-time image analysis unit as pre-stored
average pixel values of the clear pictures of the defect
pattern.
19. The image analysis and compensation method of a defect
observation machine according to claim 12, wherein an average pixel
value of a blurred in-line defect pattern picture and an average
pixel value of a clear defect pattern picture resulting from
compensation are counted to obtain a pixel difference, and a
focusing compensation value for compensating the blurred defect
pattern picture as the clear defect pattern picture is counted, so
as to obtain data of a correspondence between the pixel difference
and the focusing compensation value, and the data is pre-stored in
the real-time image analysis unit as the data of the correspondence
between the pixel difference and the focusing compensation
value.
20. The image analysis and compensation method of a defect
observation machine according to claim 12, wherein the pixel
difference comprises a plurality of ranges wherein each
corresponding to one focusing compensation value.
21. The image analysis and compensation method of a defect
observation machine according to claim 20, wherein when the pixel
difference is negative, the focusing compensation value is
positive, and when the pixel difference is positive, the focusing
compensation value is negative.
22. The image analysis and compensation method of a defect
observation machine according to claim 12, wherein the threshold is
obtained through definition experiments and analyses of various
in-line defect pattern pictures.
23. The image analysis and compensation method of a defect
observation machine according to claim 22, wherein the threshold is
10.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims priority to and the benefit
of Chinese Patent Application No. 201911133518.1 filed on Nov. 19,
2019, the disclosure of which is incorporated herein by reference
in its entirety as part of the present application.
BACKGROUND
[0002] The disclosure relates to defect observation technology, in
particular to a defect observation machine and an image analysis
and compensation method thereof.
[0003] With the development of semiconductor technology, the demand
for chip yield rate is getting higher and higher. However, the
integrated circuit chip manufacturing process is complex and many
defects are often produced during manufacturing, thus defect
observation is an essential process during manufacturing of
integrated circuits.
[0004] As the semiconductor industry technology becomes more mature
and advanced, the line width becomes smaller, the density becomes
higher, and the corresponding defects become smaller. More
requirements are put forward for defect observation. In the current
defect observation machine (SEMVision), when some special products
and special sheet material products are observed, the defect image
is often blurred. Such products with blurred images need to be
re-observed through artificial focusing. There will be a certain
impact on the production capacity of the machine. For serious
defect problems, such repeated observations cannot meet the
timeliness. For details, please refer to the defect observation
method of the prior art shown in FIG. 1.
BRIEF SUMMARY
[0005] According to embodiments described herein there is provided
a defect observation machine. The defect observation machine having
a function of analyzing and compensating for a defect pattern
picture in real time so that the defect pattern picture outputted
by the defect observation machine is a clear defect pattern
picture, wherein the defect observation machine comprises: a defect
observation unit, which is used to perform defect pattern
observation on a semiconductor product through a focusing value and
output the defect pattern picture; and a real-time image analysis
unit, which is used to pre-store an average pixel value and data of
a correspondence between a pixel difference and a focusing
compensation value of a clear defect pattern picture, receive the
defect pattern picture, perform data analysis on the defect pattern
picture to obtain an average pixel value of the defect pattern
picture, compare the average pixel value of the defect pattern
picture and the average pixel value of the corresponding clear
defect pattern picture to obtain a pixel difference, judge the
pixel difference, output the received defect pattern picture as a
final defect pattern picture if the absolute value of the pixel
difference is less than or equal to a threshold, search for a
corresponding focusing compensation value from the data of the
correspondence between the pixel difference and the focusing
compensation value according to the pixel difference if the
absolute value of the pixel difference is greater than the
threshold, output the focusing compensation value to the defect
observation unit, such that the defect observation unit adjusts a
focusing value of the defect observation unit by using the focusing
compensation value and then keeps observing the same defect pattern
and again outputs a defect pattern picture to the real-time image
analysis unit.
[0006] According to embodiments described herein there is provided
an image analysis and compensation method of a defect observation
machine, wherein the method comprises: S1: a defect observation
unit performing defect pattern observation on a semiconductor
product through a focusing value, so as to output a defect pattern
picture; S2: a real-time image analysis unit receiving the defect
pattern picture, and performing data analysis on the defect pattern
picture to obtain an average pixel value of the defect pattern
picture; S3: pre-storing in the real-time image analysis unit an
average pixel value of a clear picture of a defect pattern and data
of a correspondence between a pixel difference and a focusing
compensation value; and S4: comparing the average pixel value of
the defect pattern picture and the average pixel value of the
corresponding clear picture of the defect pattern to obtain a pixel
difference, the real-time image analysis unit judging the pixel
difference, the real-time image analysis unit outputting the
received defect pattern picture as a final defect pattern picture
if the absolute value of the pixel difference is less than or equal
to a threshold, the real-time image analysis unit searching for a
corresponding focusing compensation value from the data of the
correspondence between the pixel difference and the focusing
compensation value according to the pixel difference if the
absolute value of the pixel difference is greater than the
threshold, and outputting the focusing compensation value to the
defect observation unit, such that the defect observation unit
adjusts a focusing value of the defect observation unit by using
the focusing compensation value and then keeps observing the same
defect pattern and again outputs a defect pattern picture to the
real-time image analysis unit.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 is a defect observation method of the prior art.
[0008] FIG. 2 shows different embodiments of common blurs in a
defect pattern picture.
[0009] FIG. 3 is a schematic view of a defect observation machine
according to an embodiment of the present disclosure.
[0010] FIG. 4a is a schematic view of obtaining an average pixel
value of a defect pattern picture according to an embodiment of the
present disclosure.
[0011] FIG. 4b is a schematic view of obtaining an average pixel
value of a defect pattern picture according to an embodiment of the
present disclosure.
[0012] FIG. 4c is a schematic view of obtaining an average pixel
value of a defect pattern picture according to an embodiment of the
present disclosure.
[0013] FIG. 5a and FIG. 5b are schematic views of obtaining an
average pixel value of a clear picture of the defect pattern
according to an embodiment of the present disclosure.
[0014] FIG. 6 is a schematic view of data of a correspondence
between a pixel difference and a focusing compensation value
according to an embodiment of the present disclosure.
[0015] FIG. 7 is a schematic view of a process of analyzing and
compensating a defect image in real time by using the defect
observation machine of the present disclosure.
[0016] Reference numerals of main elements in the figures are
described as follows:
[0017] 300--defect observation machine; 310--defect observation
unit; 320--real-time image analysis unit.
DETAILED DESCRIPTION
[0018] The technical solutions in the present disclosure will be
clearly and fully described below with reference to the drawings.
Apparently, described embodiments are only part of rather than all
of the embodiments of the present disclosure. All other embodiments
obtained by those ordinarily skilled in the art based on the
embodiments in the present disclosure without creative efforts fall
within the protection scope of the present disclosure.
[0019] Specifically, please refer to FIG. 2 which shows different
embodiments of common blurred defect pattern pictures. FIG. 2a
shows a polished section site where an oxide layer is on the
surface and there are no patterns, which will easily cause a
blurred defect pattern picture. FIG. 2b shows a small type of
defect with a defect size of less than 0.2 um, which will easily
cause a blurred defect pattern picture. FIG. 2c shows that there is
a hierarchical defect and it is difficult to be focused, which will
easily cause a blurred defect pattern picture. FIG. 2d shows a
photoresist site where a thick layer of photoresist is on the
surface, which will easily cause a blurred defect pattern picture.
FIG. 2e shows a thick site of 5-ThinDEP with a defect having thick
wrapping, which will easily cause a blurred defect pattern picture.
FIG. 2f shows dense defects where there are many types of defects
on the surface, which will easily cause a blurred defect pattern
picture. Apparently, the present disclosure does not exhaust all
defect types, and all defects that easily cause a blurred defect
pattern picture are within the protection scope of the present
disclosure.
[0020] In one embodiment of the present disclosure, a defect
observation machine is provided. The defect observation machine has
a function of analyzing and compensating for a defect pattern
picture in real time so that the defect pattern picture outputted
by the defect observation machine is a clear defect pattern
picture. Specifically, a reference can be made to FIG. 3 which is a
schematic view of a defect observation machine according to one
embodiment of the present disclosure. As shown in FIG. 3, the
defect observation machine 300 comprises: a defect observation unit
310, which is used to perform defect pattern observation on a
semiconductor product through a focusing value and output a defect
pattern picture; and a real-time image analysis unit 320, which is
used to pre-store an average pixel value of a clear picture of a
defect pattern and data of a correspondence between a pixel
difference and a focusing compensation value, receive the defect
pattern picture, perform data analysis on the defect pattern
picture to obtain an average pixel value of the defect pattern
picture, compare the average pixel value of the defect pattern
picture and the average pixel value of the corresponding clear
picture of the defect pattern to obtain a pixel difference, judge
the pixel difference, output the received defect pattern picture as
a final defect pattern picture if the absolute value of the pixel
difference is less than or equal to a threshold, search for a
corresponding focusing compensation value from the data of the
correspondence between the pixel difference and the focusing
compensation value according to the pixel difference if the
absolute value of the pixel difference is greater than the
threshold, output the focusing compensation value to the defect
observation unit 310, such that the defect observation unit 310
adjusts a focusing value of the defect observation unit 310 by
using the focusing compensation value and then keeps observing the
same defect pattern and again outputs a defect pattern picture to
the real-time image analysis unit 320.
[0021] In this way, by embedding a real-time image analysis unit in
a defect observation machine, analyzing and judging a defect
pattern picture outputted by a defect observation unit in real
time, and feeding back a focusing compensation value of the blurred
defect pattern picture to the defect observation unit to perform
focusing compensation on the blurred defect pattern picture, a
clear photo is presented, thereby completing semiconductor product
operations without artificial focusing for re-observation. The
timeliness is high.
[0022] Specifically, in one embodiment of the present disclosure,
the defect observation unit 310 is an electron microscope.
Apparently, the present disclosure is not so limited, and any
device that can take a photo of a defect pattern to form a defect
pattern picture is possible.
[0023] Specifically, in one embodiment of the present disclosure,
the real-time image analysis unit 320 selects 20% of a central
portion of the received defect pattern picture, and obtains an
average pixel value of the defect pattern picture within the range
of 20% as the average pixel value of the defect pattern picture.
Specifically, a reference can be made to FIG. 4a which shows a
schematic view of obtaining an average pixel value of a defect
pattern picture according to one embodiment of the present
disclosure. Based on a research, the defect is positioned at the
20% of the central portion of the defect pattern picture, and all
information of the defect pattern can be included here. More
specifically, in one embodiment of the present disclosure, the
defect pattern picture within the range of 20% is divided into n*n
units to obtain pixel values of the n*n units for converting the
defect pattern picture within the range of 20% into data of
luminance values of n*n pixels, so as to calculate an average value
of pixel values of the data of luminance values of n*n pixels as
the average pixel value of the defect pattern picture.
Specifically, a reference can be made to FIG. 4b which shows a
schematic view of obtaining an average pixel value of a defect
pattern picture according to one embodiment of the present
disclosure. The reference numeral 410 indicates data of luminance
values of 4*4 pixels. More specifically, in one embodiment of the
present disclosure, the defect pattern picture within the range of
20% is divided into n*n units to obtain pixel values of the n*n
units for converting the defect pattern picture within the range of
20% into a histogram with abscissas indicating luminance and
ordinates indicating pixel values, so as to obtain the average
pixel value of the defect pattern picture according to the
histogram. Specifically, a reference can be made to FIG. 4c which
shows a schematic view of obtaining an average pixel value of a
defect pattern picture according to one embodiment of the present
disclosure. The reference numeral 420 indicates a histogram.
[0024] Specifically, in one embodiment of the present disclosure,
the average pixel value of the clear picture of the defect pattern
comprises average pixel values of clear pictures of the defect
pattern of defects of 90% of all in-line defect types, so that
defect pattern pictures of about 90% of defects can be made clear
through the defect observation machine 300 of the present
disclosure. Specifically, a reference can be made to FIG. 5a and
FIG. 5b which show a schematic view of obtaining an average pixel
value of a clear picture of the defect pattern according to one
embodiment of the present disclosure. As shown in FIG. 5a, a
classification situation of in-line defects in 30 days is
statistically analyzed. It is found that 17 defect types account
for about 90% of all defect types. Moreover, a reference is made to
FIG. 5b. Average pixel values of the clear pictures of the defect
pattern of the 17 defect types are obtained according to in-line
data, and are pre-stored in the real-time image analysis unit 320
as pre-stored average pixel values of the clear pictures of the
defect pattern. In this way, the defect pattern pictures of the
about 90% of defects are analyzed and judged in real time by the
real-time image analysis unit 320 shown in FIG. 3, and the focusing
compensation value of the blurred defect pattern picture is fed
back to the defect observation unit for performing focusing
compensation on the blurred defect pattern picture, so as to
present a clear photo. In this manner, the defect pattern pictures
of about 90% of defects can be made clear.
[0025] Specifically, in one embodiment of the present disclosure,
an average pixel value of a blurred in-line defect pattern picture
and an average pixel value of a clear defect pattern picture
resulting from compensation are counted to obtain a pixel
difference, and a focusing compensation value for compensating the
blurred defect pattern picture as the clear defect pattern picture
is counted, so as to obtain data of a correspondence between the
pixel difference and the focusing compensation value, and the data
is pre-stored in the real-time image analysis unit 320 as the data
of the correspondence between the pixel difference and the focusing
compensation value. Specifically, in one embodiment of the present
disclosure, the pixel difference comprises a plurality of ranges
wherein each corresponding to one focusing compensation value. In
this way, a corresponding focusing compensation value can be
selected, according to a pixel difference between an average pixel
value of each defect pattern picture and a pre-stored average pixel
value of a corresponding clear picture of the defect pattern, to
compensate for the focusing value of the defect observation unit
310. Specifically, in one embodiment of the present disclosure,
when the pixel difference is negative, the focusing compensation
value is positive, and when the pixel difference is positive, the
focusing compensation value is negative. Specifically, a reference
can be made to FIG. 6 which shows a schematic view of data of a
correspondence between a pixel difference and a focusing
compensation value according to one embodiment of the present
disclosure. As depicted in FIG. 6, when the pixel difference ranges
between negative 10 and positive 10, there is no need to compensate
for the focusing value, and the real-time image analysis 320
outputs the received defect pattern picture as the final defect
pattern picture; when the pixel differences ranges between negative
20 and positive 20, the focusing compensation value is plus or
minus 1, wherein when the pixel difference ranges between negative
20 and 0, the focusing compensation value is plus 1, and when the
pixel difference ranges between 0 and positive 20, the focusing
compensation value is minus 1; when the pixel difference ranges
between negative 30 and positive 30, the focusing compensation
value is plus or minus 3; and when the pixel difference ranges
between negative 40 and positive 40, the focusing compensation
value is plus or minus 5. In this way, a corresponding focusing
compensation value is searched from the data of the correspondence
between the pixel difference and the focusing compensation value
according to the pixel difference obtained by the real-time image
analysis unit 320, so as to convert the originally blurred defect
pattern picture as a clear defect pattern picture.
[0026] In one embodiment of the present disclosure, the threshold
is obtained through definition experiments and analyses of various
in-line defect pattern pictures. In one embodiment of the present
disclosure, the threshold is 10. The threshold may have a certain
deviation. In one embodiment of the present disclosure, the
deviation is within 20%, more preferably within 10%, even more
preferably within 5%.
[0027] In one detailed embodiment of the present disclosure, a
reference can be made to FIG. 7 which shows a schematic view of a
process of analyzing and compensating for a defect image in real
time by using the defect observation machine of the present
disclosure. The defect observation unit 310 outputs a defect
pattern picture shown in FIG. 7a. The real-time image analysis unit
320 selects 20% of a central portion of the defect pattern picture
shown in FIG. 7a, as shown in FIG. 7b, and obtains an average pixel
value of the defect pattern picture, as shown in FIG. 7c, the
average pixel value is 63. Moreover, an average pixel value
corresponding to the defect pattern picture is found in pre-stored
average pixel values of clear pictures of the defect pattern in the
real-time image analysis unit 320, as shown in FIG. 7d, the average
pixel value is 48. It is determined that the pixel difference is
15, which is greater than 10. A focusing compensation value
corresponding to the pixel difference of 15 is found to be minus 1
from data of a correspondence between pixel differences and
focusing compensation values. The focusing compensation value is
outputted to the defect observation unit 310. The defect
observation unit 310 decreases the focusing value of the defect
observation unit 310 by 1 according to the focusing compensation
value, and then keeps observing the same defect pattern and again
outputs a defect pattern picture. Then the above-mentioned steps
are repeated until the absolute value of the pixel difference is
less than the threshold of 10. The real-time image analysis unit
320 outputs the received defect pattern picture as the final defect
pattern picture.
[0028] In one embodiment of the present disclosure, an image
analysis and compensation method of a defect observation machine is
further provided. The image analysis and compensation method of a
defect observation machine comprises:
[0029] S1: A defect observation unit performing defect pattern
observation on a semiconductor product through a focusing value, so
as to output a defect pattern picture;
[0030] Specifically, in one embodiment of the present disclosure,
the defect observation unit is an electron microscope. Apparently,
the present disclosure is not so limited, and any device that can
take a photo of a defect pattern to form a defect pattern picture
is possible.
[0031] S2: A real-time image analysis unit receiving the defect
pattern picture, and performing data analysis on the defect pattern
picture to obtain an average pixel value of the defect pattern
picture;
[0032] Specifically, in one embodiment of the present disclosure,
the real-time image analysis unit selects 20% of a central portion
of the received defect pattern picture, and obtains an average
pixel value of the defect pattern picture within the range of 20%
as the average pixel value of the defect pattern picture.
Specifically, a reference can be made to FIG. 4a. Based on a
research, the defect is positioned at the 20% of the central
portion of the defect pattern picture, and all information of the
defect pattern can be included here. More specifically, in one
embodiment of the present disclosure, the defect pattern picture
within the range of 20% is divided into n*n units to obtain pixel
values of the n*n units for converting the defect pattern picture
within the range of 20% into data of luminance values of n*n
pixels, so as to calculate an average value of pixel values of the
data of luminance values of n*n pixels as the average pixel value
of the defect pattern picture. Specifically, a reference can be
made to FIG. 4b. The reference numeral 410 indicates data of
luminance values of 4*4 pixels. More specifically, in one
embodiment of the present disclosure, the defect pattern picture
within the range of 20% is divided into n*n units to obtain pixel
values of the n*n units for converting the defect pattern picture
within the range of 20% into a histogram with abscissas indicating
luminance and ordinates indicating pixel values, so as to obtain
the average pixel value of the defect pattern picture according to
the histogram. Specifically, a reference can be made to FIG. 4c.
The reference numeral 420 indicates a histogram.
[0033] S3: Pre-storing in the real-time image analysis unit an
average pixel value of a clear picture of a defect pattern and data
of a correspondence between a pixel difference and a focusing
compensation value;
[0034] Specifically, in one embodiment of the present disclosure,
the average pixel value of the clear picture of the defect pattern
comprises average pixel values of clear pictures of the defect
pattern of defects of 90% of all in-line defect types, so that
defect pattern pictures of about 90% of defects can be made clear
through the image analysis and compensation method of the defect
observation machine of the present disclosure. Specifically, a
reference can be made to FIG. 5a and FIG. 5b. As shown in FIG. 5a,
a classification situation of in-line defects in 30 days is
statistically analyzed. It is found that 17 defect types account
for about 90% of all defect types. Moreover, a reference is made to
FIG. 5b. Average pixel values of the clear pictures of the defect
pattern of the 17 defect types are obtained according to in-line
data, and are pre-stored in the real-time image analysis unit as
pre-stored average pixel values of the clear pictures of the defect
pattern.
[0035] Specifically, in one embodiment of the present disclosure,
an average pixel value of a blurred in-line defect pattern picture
and an average pixel value of a clear defect pattern picture
resulting from compensation are counted to obtain a pixel
difference, and a focusing compensation value for compensating the
blurred defect pattern picture as the clear defect pattern picture
is counted, so as to obtain data of a correspondence between the
pixel difference and the focusing compensation value, and the data
is pre-stored in the real-time image analysis unit as the data of
the correspondence between the pixel difference and the focusing
compensation value. Specifically, in one embodiment of the present
disclosure, the pixel difference comprises a plurality of ranges
wherein each corresponding to one focusing compensation value.
[0036] S4: Comparing the average pixel value of the defect pattern
picture and the average pixel value of the corresponding clear
picture of the defect pattern to obtain a pixel difference, the
real-time image analysis unit judging the pixel difference, the
real-time image analysis unit outputting the received defect
pattern picture as a final defect pattern picture if the absolute
value of the pixel difference is less than or equal to a threshold,
the real-time image analysis unit searching for a corresponding
focusing compensation value from the data of the correspondence
between the pixel difference and the focusing compensation value
according to the pixel difference if the absolute value of the
pixel difference is greater than the threshold, and outputting the
focusing compensation value to the defect observation unit, such
that the defect observation unit adjusts the focusing value of the
defect observation unit by using the focusing compensation value
and then keeps observing the same defect pattern and again outputs
a defect pattern picture to the real-time image analysis unit.
[0037] In this way, a corresponding focusing compensation value can
be selected, according to a pixel difference between an average
pixel value of each defect pattern picture and a pre-stored average
pixel value of a corresponding clear picture of the defect pattern,
to compensate for the focusing value of the defect observation
unit. Accordingly, defect pattern pictures of about 90% of defects
can be analyzed and judged in real time, and a focusing
compensation value of a blurred defect pattern picture is fed back
to the defect observation unit to perform focusing compensation on
the blurred defect pattern picture, so as to present a clear photo.
In this manner, defect pattern pictures of about 90% of defects can
be made clear.
[0038] Specifically, in one embodiment of the present disclosure,
when the pixel difference is negative, the focusing compensation
value is positive, and when the pixel difference is positive, the
focusing compensation value is negative. Specifically, a reference
can be made to FIG. 6. When the pixel difference ranges between
negative 10 and positive 10, there is no need to compensate for the
focusing value, and the real-time image analysis outputs the
received defect pattern picture as the final defect pattern
picture; when the pixel differences ranges between negative 20 and
positive 20, the focusing compensation value is plus or minus 1,
wherein when the pixel difference ranges between negative 20 and 0,
the focusing compensation value is plus 1, and when the pixel
difference ranges between 0 and positive 20, the focusing
compensation value is minus 1; when the pixel difference ranges
between negative 30 and positive 30, the focusing compensation
value is plus or minus 3; and when the pixel difference ranges
between negative 40 and positive 40, the focusing compensation
value is plus or minus 5. In this way, a corresponding focusing
compensation value is searched from the data of the correspondence
between the pixel difference and the focusing compensation value
according to the pixel difference obtained by the real-time image
analysis unit, so as to convert the originally blurred defect
pattern picture as a clear defect pattern picture.
[0039] In one embodiment of the present disclosure, the threshold
is obtained through definition experiments and analyses of various
in-line defect pattern pictures. In one embodiment of the present
disclosure, the threshold is 10. The threshold may have a certain
deviation. In one embodiment of the present disclosure, the
deviation is within 20%, more preferably within 10%, even more
preferably within 5%.
[0040] In summary, by embedding a real-time image analysis unit in
a defect observation machine, analyzing and judging a defect
pattern picture outputted by a defect observation unit in real
time, and feeding back a focusing compensation value of a blurred
defect pattern picture to the defect observation unit to perform
focusing compensation on the blurred defect pattern picture, a
clear photo is presented, thereby completing semiconductor product
operations without artificial focusing for re-observation. The
timeliness is high.
[0041] Finally, it should be noted that the above embodiments are
only used for illustrating rather than limiting the technical
solutions of the present disclosure. Although the present
disclosure has been described in detail with reference to the
foregoing embodiments, those ordinarily skilled in the art should
understand that they can still modify the technical solutions
recorded in the foregoing embodiments, or equivalently replace some
or all of the technical features thereof; and such modifications or
replacements do not deviate the essence of the corresponding
technical solutions from the scope of the technical solutions of
the embodiments of the present disclosure.
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